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PRAXORALAB Confidential
Your AI Readiness
Assessment Report
Overall Readiness Score | Dimension | Score | Status | |
|---|---|---|---|
| Data readiness | |||
| Process definition | |||
| Governance structure | |||
| Team capability | |||
| Measurement |
Deployment blocker identified
PRAXORALAB A score is only useful if it is understood, not just displayed. This section explains what your result indicates, what is already working, what closes the remaining gaps, and what is put at risk by deploying before they are closed.
PRAXORALAB What this score costs to ignore, and what to do in the next 14 days
A score is a diagnosis, not a plan. This section turns your result into what is actually at stake if the gap goes unaddressed, and a short, sequenced list of what to do next, starting this week.
95%
of generative AI pilots fail to produce a measurable profit-and-loss result
MIT NANDA, The GenAI Divide, 2025
60%
of AI projects fail on poor data foundations, and over 40% of agent projects fail on legacy-system incompatibility, both detectable before a dollar is spent
Gartner, 2025
74%
of companies have yet to show tangible value from their AI investment, despite the spend behind it
BCG, Where's the Value in AI?, October 2024
33%
of companies have scaled AI company-wide, against 78% who use it frequently, the gap this framework exists to close
McKinsey, The State of AI, 2024
PRAXORALAB What each dimension measures, and what the research says
Each dimension is self-rated on a fixed 0–3 rubric, not a subjective impression. A score of 0–1 signals a deployment blocker; 2–3 signals the dimension is on track. Each definition below is paired with a named, publicly reported research finding for context, not to describe your specific organisation.
01 Data readiness
Definition — Whether the data behind the process you want AI to touch is accessible, documented, current, and owned by someone specific.
Scoring basis — 0 = scattered across systems with no single source of truth; 3 = clean, documented, current, and owned.
Why it matters — Every other dimension depends on this one. If the underlying data cannot be trusted, nothing built on top of it, including governance and measurement, can be trusted either.
Research 60% of AI projects will be abandoned by 2026 for lack of AI-ready data, Gartner projects. Gartner, February 2025.
02 Process definition
Definition — How consistently and explicitly the target process is documented, versus how much it still depends on individual judgement.
Scoring basis — 0 = lives mostly in people's heads; 3 = documented clearly enough that a new hire could follow it unaided.
Why it matters — AI automates what is defined. An undocumented process does not become predictable because AI is layered onto it. It becomes unpredictable faster.
Research 2 in 3 leaders call their own organisation overly complex, and simplifying the process is the bigger lever, McKinsey finds, not layering technology onto it unchanged. McKinsey, The State of Organizations 2026.
03 Governance structure
Definition — Whether a named reviewer and a defined escalation path exist for catching and correcting AI output before it reaches a customer or a decision.
Scoring basis — 0 = no review step exists at all; 3 = a named reviewer and a clear escalation path both exist.
Why it matters — Governance determines how much damage an error does before someone notices it. It is the control layer that makes fast deployment survivable rather than reckless.
Research 63% of companies using generative AI have no governance structure in place to manage the risks it creates. McKinsey, The State of AI, 2024.
04 Team capability
Definition — Whether staff working with the system can independently operate, review, question and correct its outputs, without outside help.
Scoring basis — 0 = would depend entirely on a vendor or consultant; 3 = a trained team is authorised to run and correct it.
Why it matters — A capable team is the last line of defence against a silent error. Without it, oversight exists on paper only, and a black box nobody understands is a dependency, not a capability.
Research 35% of staff received AI training in the past year, even though 75% of companies are adopting AI. Randstad, AI Skills Gap, November 2024.
05 Measurement
Definition — Whether the business outcome the AI system is meant to move is baselined before deployment begins, so any later change can be credibly attributed to it.
Scoring basis — 0 = no baseline exists, impact could only be guessed at; 3 = a specific baseline is set before deployment begins.
Why it matters — Without a baseline set before deployment, no later improvement can be credibly attributed to the AI system at all, and the business case becomes unverifiable after the fact.
Research 74% of companies have yet to show tangible value from their AI investment, despite the spend behind it. BCG, Where's the Value in AI?, October 2024.
This report is a self-assessment based on how you rated your own organisation on the live tool, not an independent audit. It is not a guarantee of deployment success, and the guidance in this report is a starting point, not a project plan. The research citations above describe general, publicly reported findings from the named sources, not a claim about your organisation specifically. Your answers were passed to this page through the page address and were not reviewed, stored, or transmitted to Praxora Lab or any third party.
PRAXORALAB What to do in each dimension
Your score in each dimension determines the specific next action. Dimensions scoring 0–1 are deployment blockers, address them before selecting tools or committing budget. Dimensions scoring 2–3 are on track.
Data readiness
Data underpins every other dimension. Before anything else, get one process's data into a single, documented, current source, even if the rest of the estate stays messy for now.
Process definition
An undocumented process does not become predictable because AI is layered onto it. It becomes unpredictable faster. Write the process down before you automate it.
Governance structure
Name a reviewer and a defined escalation path before anything goes live, not after something goes wrong.
Team capability
A tool nobody on staff can operate, review or correct is a dependency, not a capability. Build the internal skill before you scale the deployment.
Measurement
Without a baseline set before deployment, no later improvement can be credibly attributed to the AI system at all. Set it now, before the next pilot starts.
Terence Kok
Enterprise AI Strategist · Former Chief AI and Innovation Officer, Meinhardt Group · Author of the five-dimension readiness framework
Terence Kok is an enterprise AI strategist and former Chief AI and Innovation Officer at Meinhardt Group, with twenty-five years leading AI and digital transformation programmes across Asia and the Middle East. The five-dimension framework this report scores is the same model he runs live during the AI Governance & ROI Executive Programme at Praxora Lab.
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The Executive Programme runs this framework live, benchmarks your score, and delivers a twelve-week roadmap.